Proceedings ofSPIE 9407, Video Surveillance and Transportation Imaging Applications 2015
We propose a real time person identification algorithm for surveillance based scenarios from low-resolution streaming video, based on mid-level features extracted from the joint distribution of various types of human actions and human poses.
The proposed algorithm uses the combination of an auto-encoder based action association framework which produces per-frame probability estimates of the action being performed, and a pose recognition framework which gives per-frame body part locations.
The main focus in this manuscript is to effectively combine these per-frame action probability estimates and pose trajectories from a short temporal window to obtain mid-level features. We demonstrate that these mid-level features captures the variation in the action performed with respect to an individual and can be used to distinguish one person from the next. Preliminary analysis on the KTH action dataset where each sequence is annotated with a specific person and a specific action is provided and shows some interesting results which verify this concept.
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Copyright © 2015, Society of Photo-optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited.
Society of Photo-optical Instrumentation Engineers
Place of Publication
San Francisco, CA
Nair, Binu M. and Asari, Vijayan K., "Person Identification from Streaming Surveillance Video using Mid-Level Features from Joint Action-Pose Distribution" (2015). Electrical and Computer Engineering Faculty Publications. 381.